Blow molder control systems and methods

ABSTRACT

Systems and methods control the operation of a blow molder. An indication of a crystallinity of at least one container produced by the blow molder may be received along with a material distribution of the at least one container. A model may be executed, where the model relates a plurality of blow molder input parameters to the indication of crystallinity and the material distribution and where a result of the model comprises changes to at least one of the plurality of blow molder input parameters to move the material distribution towards a baseline material distribution and the crystallinity towards a baseline crystallinity. The changes to the at least one of the plurality of blow molder input parameters may be implemented.

PRIORITY CLAIM

The present application is a continuation of U.S. patent applicationSer. No. 18/049,656, filed Oct. 26, 2022, which is a continuation ofU.S. patent application Ser. No. 17/495,421, filed Oct. 6, 2021, whichis a continuation of U.S. patent application Ser. No. 16/245,805, filedJan. 11, 2019, which is a continuation of U.S. patent application Ser.No. 15/840,774, filed Dec. 13, 2017, now U.S. Pat. No. 10,183,440,issued on Jan. 22, 2019, which is a continuation of U.S. patentapplication Ser. No. 15/367,392, filed Dec. 2, 2016, now U.S. Pat. No.9,868,247, issued Jan. 16, 2018, which is a continuation of U.S. patentapplication Ser. No. 14/652,383, filed Jun. 15, 2015, now U.S. Pat. No.9,539,756, issued Jan. 10, 2017, which is a National Stage ofInternational Application Serial No. PCT/US2014/050734, filed Aug. 12,2014, which claims the benefit of U.S. provisional patent applicationSer. No. 61/864,905, filed Aug. 12, 2013, which are all incorporatedherein by reference in their entirety.

BACKGROUND

Polyethylene terephthalate (PET) and other types of plastic containersare commonly produced utilizing a machine referred to as a reheat,stretch and blow molder. The blow molder receives preforms and outputscontainers. When a preform is received into a blow molder, it isinitially heated and placed into a mold. A rod stretches the preformwhile air is being blown into the preform causing it to stretch axiallyand circumferentially, and take the shape of the mold. A typical reheat,stretch and blow molder has between ten (10) and forty-eight (48) ormore molds. This increases the product rate of the blow molder, but alsoincreases the rate at which defective containers can be generated whenthere is a problem with one or more blow molding process parameters.Accordingly, container manufacturers are keen to detect and correct blowmolding process problems as efficiently as possible.

In the course of manufacturing blow-molded containers, it is desirableto control the blow molder to achieve desired container propertiesincluding, desired container dimensions, material distribution,strength, the absence of defects, etc. This is typically accomplishedmanually. According to one common technique, an operator of the blowmolder ejects a set of completed containers for off-line inspection.Various types of off-line inspections are used to measure differentaspects of the container. Material or thickness distribution is oftenmeasured using a qualitative “squeeze” test and/or a quantitativesection weight test. In a squeeze test, the operator, or other testingpersonnel, squeezes the container to obtain a qualitative indication ofwhether there is sufficient material at key locations of the container.In a section weight test, the container is physically divided intocircumferential sections. Each section is individually weighted,yielding the section weights. Other common off-line inspections includetop load and burst pressure tests to measure container strength,volumetric fill height and base clearance tests to measure containersize and shape, etc. Based on the qualitative and quantitative resultsof tests such as these, the operator modifies input parameters of theblow molder to move material to the appropriate locations within thebottle.

Manual measurement and adjustment techniques, however, suffer fromseveral disadvantages. The qualitative nature of some of the tests makesit difficult to maintain consistent results from one tester to another.For example, different operators may interpret the same materialdistribution differently during a squeeze test. Also, even when using arig, it is difficult to precisely replicate section cuts across multiplecontainer samples, reducing the accuracy of manually obtained sectionweights. Further, it is very difficult for operators to consistentlytune the blow molder to obtain the desired material distribution. Thecorrelations between blow molder input parameters and output materialdistribution are very complex. Different blow molder operators havediffering levels of understanding of these parameters and, therefore,differing abilities to obtain desired container distributions. As aresult, many operators simply avoid modifying blow molder inputparameters unless the containers are outside of design tolerances, evenif an alternative material distribution would be desirable.

Early on-line inspection systems, such as the Intellispec™ product,available from Pressco Technology Inc. of Cleveland Ohio and thePET-View product, available from the Krones Group of Neutraubling,Germany, utilize computer vision to inspect containers either in ordownstream of the blow molder and reject mal-formed containers. Thesesystems improve the quality of the container production by removingcontainers with randomly occurring damage, inclusions, and grosslyformed containers, but have limited success addressing process relatedissues that drive container quality and performance.

Subsequent inspection devices have allowed more detailed inspections todetect more subtle system properties. For example, the various infraredabsorption measurement devices available from AGR International ofButler, Pa., are capable of measuring the material distribution ofindividual containers. The measurements are made using a series ofemitters and sensors that are located either within or downstream of theblow molder. The sensors are oriented towards the sidewalls of thecontainers and generate measurements on the containers at 12.5 mmintervals, thus providing a profile of material distribution in thecontainer sidewalls. Measurements from devices such as the AGR infraredabsorption measurement devices obviate the need to conduct squeeze andsection weight tests while, at the same time, providing more repeatableresults. Also, advanced vision systems, such as the Pilot Vision™system, also available from AGR International, Inc. of Butler, Pa.,provide increased resolution and are able to detect more subtlecontainer defects.

Recent advances in blow molder technology have allowed for some degreeof automated process control in blow molders. For example, many currentblow molders have mechanical mold controls that may be operatedutilizing servo motors and other smart technology. Oven designs andcontrol improvements have also improved. Also, recently the Sidel S.A.S.Company of Le Havre, France, has introduced a blow molder with a moldcontrol loop that to accommodate variations in the temperature ofperforms arriving at the mold. The mold control loop controls thepre-blow start and pre-blow pressure to detect changes in preformproperties and adapts the pre-blow pressure profile to account for anyvariations in preform energy or energy distribution.

Another process control system is the Process Pilot® product, availablefrom AGR International, Inc. of Butler, Pa. The Process Pilot® productis a closed loop control system used to manage the re-heat stretch andblow molding process. An infrared absorption-type measurement system isused to generate a material distribution profile, as described above.The Process Pilot® product learns the relationship between the containerblowing process and the location of the material in the container with aseries of automated measurements made in conjunction with adjustments tothe blow molder input parameters. This information forms the basis forfuture adjustments to the blow molder. A custom equation is used toexpress the relationship between blow molder input parameters andresulting material distributions. A control loop is implemented byestablishing a baseline material distribution and baseline values forthe various blow molder inputs. As the material distribution driftsduring the blow molding process, relationship between the blow molderinput parameters and container characteristics is utilized inconjunction with additional mathematics to determine blow molderparameter values that minimize the difference between the baseline andthe measured material distribution while also minimizing control changesrelative to baseline blow molder input parameters. The Process Pilot®product can be operated continuously to minimize the overall processvariation.

Current blow molder process control systems represent an improvementover the prior off-line and often manual methods. Additional challengesremain, however. For example, current control systems described above donot consider container properties such as crystallinity or materialdensity, base sag, various container dimensions, etc. These and otherproperties, which can have a significant effect on overall containerquality, must still be managed with manual off-line tests and manualadjustments to blow molder input parameters. Improved process controlsystems for blow molders are needed.

FIGURES

Various embodiments are described herein by way of example inconjunction with the following figures, wherein:

FIG. 1 is a block diagram showing one embodiment of a blow moldersystem.

FIG. 2 is a block diagram of one embodiment of a blow molder controlsystem.

FIG. 3 illustrates one embodiment of a material distribution measuringdevice that may be associated with the material distribution system.

FIG. 4 is a block diagram showing one embodiment of a base visionsystem.

FIG. 5 is a block diagram showing one embodiment of a sidewall visionsystem.

FIG. 6 is a block diagram showing one embodiment of a finish visionsystem.

FIG. 7 is a diagram showing example finish dimensions that may bemeasured utilizing the finish vision system.

FIG. 8 is diagram showing an image of the container illustrating variousmethods for determining clarity status.

FIG. 9 is a diagram showing one embodiment of a base temperature sensorsystem.

FIG. 10 is a diagram showing one embodiment of a birefringence sensorsystem for measuring crystallinity and/or orientation.

FIG. 11 is a diagram showing one embodiment of a near infrared (NIR)spectroscopy sensor system for measuring crystallinity.

FIG. 12 is a flow chart showing one embodiment of a process flow thatmay be executed by the system to generate a crystallinity model thatrelates NIR absorption spectra to crystallinity.

FIG. 13 is a chart showing one embodiment of a set of calibrationspectra.

FIG. 14 is a chart showing one embodiment of the calibration spectra ofFIG. 13 after conditioning.

FIG. 15 is a flow chart showing one embodiment of a process flow fordetermining the crystallinity of a container in-line utilizing the modeldetermined by the process flow of FIG. 14 .

FIG. 16 is a block diagram showing one embodiment of a work flow thatmay be executed by the control system to control the blow molder system.

FIG. 17 is flow chart showing one embodiment of a process flow fortraining the model of FIG. 16 .

FIG. 18 is a flow chart showing one embodiment of a process flow thatmay be executed by the control system to apply the model of FIG. 16 tomodify the blow molder input parameters based on the containercharacteristics of containers produced by the blow molder system.

FIG. 19 is a flow chart showing one embodiment of the work flow of FIG.16 including additional input container characteristics.

FIG. 20 is a block diagram showing one embodiment of a work flow thatmay be executed by the control system to control the blow molder systemutilizing two control loops.

FIG. 21 is a flow chart showing one embodiment of a set of process flowsfor executing the material distribution control loop and preformtemperature control loop shown in FIG. 20 in a cold mold process.

FIG. 22 is a flow chart showing one embodiment of a process flowrepresenting an alternate implementation of the preform temperaturealgorithm of the preform temperature control loop of FIG. 20 that alsoconsiders a container base temperature in a cold mold process.

FIG. 23 is a flow chart showing one embodiment of a process flowrepresenting an alternate implementation of the preform temperaturealgorithm of the preform temperature control loop of FIG. 20 in a hotmold configuration.

FIG. 24 is a flow chart showing one embodiment of a process flowrepresenting an alternate implementation of the preform temperaturealgorithm of the preform temperature control loop of FIG. 20 in a hotmold configuration and including additional actions that correct forcontainer dimensions.

DESCRIPTION

Various embodiments described herein are directed to systems and methodsfor controlling blow molder input parameters to optimize containercharacteristics such as, for example, crystallinity, base sag, andvarious container dimensions. Existing blow molder process controlsystems, such as the Process Pilot® product described above, are capableof optimizing the material distribution of containers. There is a need,however, for systems and methods for also optimizing other containerproperties such as those referenced above.

Crystallinity in a PET container describes degree of orientation in thePET molecules for that container. Crystallinity in typical PETcontainers ranges from ˜20% to ˜40%, depending on the type of container.The degree of crystallinity in a container affects many otherquality-related characteristics of a container including its structuralstrength including top load performance, clarity (e.g., haze orpearlescence), permeability, etc. Therefore, optimizing crystallinitymay also effectively optimize crystallinity-related characteristics.Crystallinity may be created and optimized differently based on the typeof blow molding process utilized (e.g., a cold mold process or a hotmold process).

In a cold mold process, bi-axial crystallinity is induced by mechanicalstretching and blowing, also referred to as strain hardening. In such aconfiguration, the preform is heated prior to reaching the mold. At themold, a stretch rod and compressed air are utilized to expand thepreform within the mold. The mold itself is typically cooled, forexample, with chilled water or another cooled fluid. The stretchingprocess increases the crystallinity of the PET molecules, but alsocreates residual stresses in the containers. The residual stresses aresubstantially set by contact with the cold mold. For cold mold-generatedcontainers, crystallinity is also sometimes referred to as orientation.The crystallinity or orientation of a cold mold-generated containerdescribes a degree to which molecules of the container are organizedaccording to the bi-axial lattice structure. For example, thecrystallinity or orientation of a cold mold-generated container mayindicate a portion or percentage of the molecules in the container thatare part of a bi-axial lattice structure.

Examples of containers generated utilizing cold mold processes includewater bottles and carbonated soft drink (CSD) containers. CSD bottlestypically have a higher level of mechanically induced crystallinity ororientation to provide greater container performance. Under ordinaryconditions, the level of crystallinity or orientation generated in coldmold processes can be managed by manipulating the temperature ofpreforms at the exit of the oven. When a preform has the proper overalltemperature or energy and energy distribution prior to blowing, there-heat stretch and blow process can be optimized to generate desiredlevels of crystallinity or orientation. However, if the preform energyor energy distribution varies, unwanted results can occur. For example,when the preform is too cold, pearlescence can occur. Pearlescencemanifests as a white chalky substance primarily on the inner portions ofthe container in regions where the greatest stretching occurs. Forexample, pearlescence may occur when bi-axial lattice structures for theclaim break. According to various embodiments, optimal levels ofcrystallinity or orientation are obtained by lowering the energy in thepreform to just above the point where the pearlescence is seen after thestretching process.

In a hot mold process, the molds of the blow molder are heated, often byhot oil or another heated fluid that is circulated through the molds.Hot mold-generated containers exhibit spherulitic crystallinity. Ameasure of crystallinity in a hot mold-generated container, for example,may indicate a degree to which molecules of the container are organizedaccording to spherulite crystal structures. For example, thecrystallinity of a hot mold generated container may indicate portion orpercentage of molecules in the container that are part of spherulitestructures. In a hot mold process, container crystallinity is createdand preserved by both mechanical and thermal forces. Hot mold processesgenerally result in higher crystallinity and therefore superior and morestable container characteristics. For example, the heat of the moldcauses spherulitic crystallization and also anneals the container, whichreleases residual stresses that could cause later deformation. Inaddition, the higher temperature levels can provide a greater mold fillfactor for heat set and non-carbonated soft drinks. Because the hot moldprocess releases rather than sets residual stresses, hot mold generatedcontainers often hold their shape better than cold molded containers,making their properties more durable and more suitable for hot liquids.As with cold mold processes, crystallinity in hot mold processes can bemanaged by controlling the preform temperature, either as set by theovens or by the hot mold itself. Higher preform energy generally leadsto higher container crystallinity and, therefore, a higher top load,better volumetric fill, etc. If preform energy is too high, however,thermal hazing can occur. Thermal hazing, or just hazing, manifests as acloudy form, called haze, which presents in a PET container. Hazing iscaused when the combination of preform and mold temperature is highenough to generate excess spherulitic crystallization, leading to lightscattering and eventually opacity. Haze affects the aesthetic propertiesof the container and may also indicate brittleness. In a hot moldprocess, it is often optimal to achieve a degree of crystallinity thatis close to, but less than the level corresponding to the onset ofthermal hazing. Therefore, in various embodiments, optimal crystallinitymay be achieved by increasing the energy in the preform to just belowthe onset of thermal hazing.

As used herein, the term crystallinity refers both cold mold-generatedcontainers and hot mold-generated containers. For example, whenreferring to a cold mold-generated container, the term crystallinity mayrefer to the orientation or degree of bi-axial lattice structuring.Also, for example, when referring to a hot mold-generated container, theterm crystallinity may refer to the degree of spherulite structuring.

Base sag is another container property that can become problematic,especially but not exclusively in cold mold processing. It is believedthat base sag is correlated with container stress cracking. Stresscracking is a particular problem for carbonated soft drink (CSD)containers, and often in the containers' petaloid bases. Stress crackingis believed to be caused by contact between containers and certainchemicals during the handling and storage that occurs after filling.Minimizing or eliminating base sag may reduce the container area that isin contact with the container's resting surface and thereby minimizecontact with potentially harmful chemistry.

Container dimensions are also affected by the blow molding process andcannot be adequately controlled with current process control systems.The need to optimize container dimensions may be particularly relevantwhen utilizing a “blow and trim” process. According to a blow and trimprocess, the finish of the container is formed within the mold itself.This means that the mold fill factor (e.g., the degree to which thecontainer fills out the mold) determines the correct formation of thefinish, including threads that must interface with a separatelymanufactured cap. Examples of the blow and trim process are provided inU.S. Pat. No. 7,455,914, which is incorporated herein by reference inits entirety.

The blow molder process control systems and methods described herein aredirected to controlling blow molder input parameters to optimizecontainer characteristics including, for example, crystallinity,orientation, base sag, and/or various container dimensions. In variousembodiments, a blow molder control system is in communication with ablow molder as well as with one or more inspection systems for measuringcharacteristics of containers generated by the blow molder. Theinspection systems may include various vision systems for capturingimages of the different portions of the container, a temperature sensorfor sensing the temperature of various portions of the container, and/ora material distribution system, such as the infrared absorptionmeasurement devices described above, for measuring a distribution ofmaterial within the container.

In a cold mold process, the container crystallinity may be managed bymodifying the preform temperature set point. For example, a visionsystem may capture images of containers generated by the blow molder.The vision system and/or the control system may process the images todetermine whether pearlescence is present. If pearlescence is present,the control system may increase the preform temperature set point. Forexample, the control system may incrementally increase the preformtemperature set point until pearlescence is no longer present in thegenerated containers. Also, in some embodiments, crystallinity may bemeasured directly in addition to or instead of measuring the presenceand/or quantity of pearlescence. For example, crystallinity may bedirectly measured utilizing a birefringence method, X-ray diffraction,Raman spectroscopy, near infrared (NIR) absorption spectroscopy, etc.When crystallinity in a cold mold process is measured directly, thecontrol system may modify the preform temperature set point and/or otherblow molder inputs to drive the crystallinity to a desired value and/orrange of values.

In various embodiments, the control system, operating with a cold moldprocess, may also consider a temperature of the produced container. Thistemperature may be measured on any suitable portion of the bottle, suchas, for example, a base temperature. Also, in some embodiments,container temperature is indirectly monitored based on a temperature ofthe mold. The temperature may be received from the temperature sensor.In some embodiments, the control system responds to changes in thecontainer temperature by making corresponding changes to the preformtemperature set point. Also, in various embodiments, the control systemis programmed to execute a control sequence for reorienting the processat or near the optimal crystallinity. For example, the control systemmay begin to decrease the preform temperature set point untilpearlescence is detected, and subsequently increase the preformtemperature set point until pearlescence is no longer present. This mayensure that the blow molder generates containers at or close to theoptimal cold mold crystallinity. The reorienting control sequence may beexecuted, for example, at the initiation of the process and/or at upondetection of a change in container temperature (e.g., an increase incontainer temperature). A reorienting control sequence may also beutilized in embodiments where crystallinity is measured directly. Forexample, the control system may decrease the preform temperature setpoint until a predetermined crystallinity is reached. The predeterminedcrystallinity may be associated with the onset of pearlescence or, insome embodiments, may indicate the container is near a thresholdcrystallinity where pearlescence occurs. Subsequently, the preformtemperature set point may be increased, for example, by a predeterminedtemperature increment, until a desired crystallinity reading isachieved, until crystallinity falls by a predetermined amount, etc.

In a hot mold process, the container crystallinity may be managed, againby modifying the preform temperature set point and/or modifying thetemperature of the mold, where such modifications are allowed by theblow molder. Again, a vision system captures images of containersgenerated by the blow molder. The vision system and/or control systemprocesses the images to determine whether thermal hazing (e.g., “haze”)is present. When haze is detected, the control system may decrease thepreform temperature set point. In various embodiments, the controlsystem incrementally decreases the preform temperature set point untilhaze is no longer present in the generated containers. If the moldtemperature is separately controllable, then the mold temperature may bemodified in addition to or instead of modifying the preform temperatureset point. It will be appreciated that, when the systems and methodsdescribed herein are utilized in conjunction with a blow molder thatallows for control of the mold temperature, changes described as beingto preform temperature set may be equivalently implemented by changes tothe mold temperature and/or changes to combinations of blow molder inputparameters including the mold temperature and the preform temperatureset point.

Also in a hot mold process, crystallinity may be measured directly inaddition to or instead of measuring the presence or amount of haze. Forexample, the control system may modify the preform temperature set pointand/or mold temperature to drive the crystallinity of the containers toa desired value or range of values.

In various embodiments, the control system, operating in a hot moldprocess, may also utilize a measurement of container temperature or moldtemperature to detect process drift. The container temperature may betaken at any portion of the container including, for example, asidewall. The measurements may be made by a separate temperature sensorin communication with the control system and/or may be received directlyfrom the blow molder. As in the cold mold process, the control systemmay modulate the preform temperature set point up or down to correspondto changes in the container temperature. In some embodiments, thecontrol system may also execute a reorienting control sequence to placethe process at or near the optimal hot mold crystallinity. For example,the control system may either allow or deliberately cause the containertemperature to rise until the onset of haze, and subsequently reduce thepreform temperature set point until haze is no longer present. This mayensure that the blow molder generates containers at or close to theoptimal hot mold crystallinity. The reorienting control sequence may beexecuted at the initiation of the process, upon detection of an increaseor decrease in container temperature, etc.

A reorienting control sequence may also be utilized in embodiments wherecrystallinity is measured directly. For example, the control system mayallow or cause the container temperature to rise until a predeterminedcrystallinity is reached. The predetermined crystallinity may beassociated with the onset of haze or, in some embodiments, may indicatethat the container is very near a threshold crystallinity where hazeoccurs. Subsequently, the preform temperature set point may bedecreased, for example, by a predetermined temperature increment, untila desired crystallinity reading is achieved, until crystallinity fallsby a predetermined amount, etc.

In various embodiments, such as those utilizing a hot mold process, thecrystallinity of generated containers may be derived from measuring atemperature of the mold. For example, changes in the temperature of themold may indicate corresponding changes to the crystallinity of producedcontainers. The temperature of the mold may be measured by a temperaturesensor with results provided to the control system. The control systemmay compensate for mold temperature changes by adjusting the preformtemperature set point to maintain the desired crystallinity level.

In various embodiments, base sag may be optimized by correcting forexcessive material and/or temperature in the base. For example, a blowncontainer base with either too much energy or material can cause thebase to sag putting this container at higher risk of contact withundesirable chemical reagents. In various embodiments, the controlsystem may periodically measure the base clearance of producedcontainers to determine if the process is creating containers that willsag. Base clearance may be measured, for example, by a vision systempositioned to capture images of the container base. When insufficientbase clearance is detected, the control system may adjust (e.g.,decrease) the preform temperature set point to compensate. In someembodiments (e.g., embodiments utilizing a cold mold process), thecontrol system may modify a temperature of a base cup portion of themold in addition to or instead of modifying the preform temperature setpoint. As described herein, this may serve to mitigate some of the riskof stress cracking. Also, in various embodiments, the control system mayperiodically measure a temperature of the bases of produced containers,for example, based on a signal from a temperature sensor. If thetemperature exceeds a threshold, then the preform temperature set pointmay be reduced. For example, if the base has too much material, the timein the mold may be insufficient to cool the base to the point where itwill not sag. Likewise if the mold base cup is too warm even the correctamount of material in the base will have a tendency to sag.

The control system may similarly control the blow molder in both hot andcold mold processes to optimize container dimensions, such as finishdimensions. For example, container dimensions may be determined by thedegree to which the preform conforms to the shape of the mold during theblowing process (e.g., the “fill-factor”). If the preform does notcontain enough energy (e.g., is not hot enough), then it may notcompletely fill the mold, leading to a container withsmaller-than-desired dimensions. Accordingly, one or more of the variousvision systems may capture images of the generated containers. Thevision systems and/or the control system may derive from the imagesdimensions of the generated containers. If the dimensions are less thana threshold, then the control system may increase the preformtemperature set point.

As described herein, the crystallinity, base sag and various containerdimensions may be controlled by adjusting the temperature of preformsduring the molding process, either by modifying the blow molders preformtemperature set point or, where supported, by modifying the temperatureof all or a part of the mold. Modifying the mold temperature and/orpreform temperature set point in isolation, however, can causesignificant and often undesired changes in material distribution.Therefore, various embodiments of the systems and methods describedherein are implemented in conjunction with a material distributioncontrol loop for monitoring material distribution. In some embodiments,a preform temperature control loop for controlling the preformtemperature set point or mold temperature is executed in conjunctionwith the material distribution control loop. The separate control loopsmay control different subsets of the blow molder input parameters. Forexample, the preform temperature control loop may control the preformtemperature set point and/or the mold temperature. The materialthickness control may control other blow molder input parametersexcluding those controlled by the preform temperature control loop (e.g.oven lamp settings, pre-blow timing, pre-blow pressure, high pressureblow timing, high pressure blow pressure, etc.). Also, in someembodiments, the control system may implement a single control loop forcontrolling all blow molder parameters, including the preformtemperature set point, as described herein to manage crystallinity, basesag, and/or container dimensions.

Before describing the control systems and methods in more detail, anoverview of a blow molder system is provided. FIG. 1 is a block diagramshowing one embodiment of a blow molder system 4 according to variousembodiments. The blow molder system 4 includes a preform oven 2 thattypically carries the plastic preforms on spindles through the ovensection so as to preheat the preforms prior to blow-molding of thecontainers. The preform oven 2 may comprise, for example, infraredheating lamps or other heating elements to heat the preforms above theirglass transition temperature. Many blow molders 6 utilize preform ovensdefining multiple heating elements positioned to heat different portionsof the preforms. The preforms leaving the preform oven 2 may enter theblow molder 6 by means, for example, of a conventional transfer system 7(shown in phantom).

The blow molder 6 may comprise a number of molds, such as on the orderof ten to twenty-four, for example, arranged in a circle and rotating ina direction indicated by the arrow C. The preforms may be stretched inthe blow molder 6, using air and/or a core rod, to conform the preformto the shape defined by the mold. In many blow molders 6, an initialpre-blow is utilized to begin the container formation process followedby a high-pressure blow to push the now-stretched walls of the preformagainst the mold. Depending on the type of container to be generated,the molds may be heated (a hot mold process) or cooled (a cold moldprocess). Containers emerging from the blow molder 6, such as container8, may be suspended from a transfer arm 10 on a transfer assembly 12,which is rotating in the direction indicated by arrow D. Similarly,transfer arms 14 and 16 may, as the transfer assembly 12 rotates, pickup the container 8 and transport the container through the inspectionarea 20, where it may be inspected by one or more of the inspectionsystems described below. A reject area 24 has a reject mechanism 26 thatmay physically remove from the transfer assembly 12 any containersdeemed to be rejected. In some embodiments, the blow molder system 4 mayinclude alternate inspection areas.

In the example of FIG. 1 , container 30 has passed beyond the rejectarea 24 and may be picked up in a star wheel mechanism 34, which isrotating in direction E and has a plurality of pockets, such as pockets36, 38, 40, for example. A container 46 is shown in FIG. 1 as beingpresent in such a star wheel pocket. The containers may then betransferred in a manner known to those skilled in the art to a conveyeror other transport mechanism according to the desired transport path andnature of the system. It will be appreciated that the blow molder system4 may comprise one or more inspection areas in addition to or instead ofthe inspection area 20. For example, alternate inspection areas may becreated by adding additional transfer assemblies, such as transportassembly 12. Also, alternate inspection areas may be positioned on aconveyor or other position down-line from the blow molder 6.

The blow molder system 4 may produce containers at a rate of 20,000 to120,000 per hour, though manufacturers continue to develop blow molderswith increasing speed and in some embodiments it may be desirable to runthe blow molder system 4 at lower rates. The blow molder system 4receives various inputs parameters that affect the characteristics ofthe generated containers. For example, the preform oven 2 may receive anoverall temperature input parameter, referred to as a preformtemperature set point, as well as additional input parameters thatdefine the distribution of heat between the individual heating elements.Other controllable parameters include, for example, a pre-blow timing, apre-blow pressure, etc.

FIG. 2 is a block diagram of one embodiment of a blow molder controlsystem 100. The system 100 comprises the blow molder system 4, a controlsystem 102, and various inspection systems 103. The inspection systems103 are positioned to sense characteristics of containers produced bythe blow molders. The inspection systems 103 may be placed in-line tosense characteristics of the containers as they are produced by the blowmolder system 4. The control system 102 may comprise one or more serversor other computer devices. The control system 102 receives signals fromthe various inspection systems 103 indicating container characteristicsand generates blow molder input parameters or changes thereto to causethe blow molder system 4 to generate containers within desiredtolerances, as described herein below.

Various different types of inspection systems 103 may be used. Forexample, a material distribution system 106 may measure a materialdistribution profile of the container. According to various embodiments,the material distribution system 106 finds the material distribution ofcontainers after formation (e.g., either in or downstream of the blowmolder system 4). For example, the material distribution system 106 maybe used to take multiple direct or indirect readings of one or morecontainer characteristics across a profile (e.g., a vertical profile) ofthe container. The container characteristics may comprise, for example,wall thickness (e.g., average 2-wall thickness), mass, volume, etc.Material distribution may be derived from any of these measurements. Thesystem 106 may utilize measured container characteristics found acrossthe profile of the container to derive a material distribution of thecontainer. In some, but not all, embodiments, the measurements, andtherefore the calculated material distribution, need only be takenacross the oriented or stretched parts of the container and may excludenon-oriented portions of the container such as, for example, a finisharea, a base cup, etc. Calculations for converting raw measurements to amaterial distribution may be performed by on-board computing equipmentassociated with the system 106 and/or by the control system 102.

The material distribution system 106 may utilize any suitable type ofmeasurement device capable of measuring a material distribution profile.For example, FIG. 3 illustrates one embodiment of a measuring device 50that may be associated with the material distribution system 106. Themeasuring device 50, may be an in-line inspection system that inspectsthe containers as they are formed, as fast as they are formed, withouthaving to remove the containers from the processing line for inspectionand without having to destroy the container for inspection. Theinspection system 50 may determine characteristics of each containerformed by the blow molder system 4 (e.g., average 2-wall thickness,mass, volume, and/or material distribution) as the formed containers arerotated or otherwise transported through an inspection area 21 followingblow molding. The inspection area 21 may be positioned similar to theexample inspection area 20 shown in FIG. 1 and/or at any other suitablein-line location, for example, as described above. Following blowmolding, containers, such as the container 66, are passed through theinspection area 21 by any suitable mechanism such as, for example, atransfer assembly such as the transfer assembly 12, a conveyor, etc.

As shown in these FIG. 3 , the inspection system 50 may comprise twovertical arms 52, 54, with a cross bar section 56 there between at thelower portion of the arms 52, 54. One of the arms 52 may comprise anumber of light energy emitter assemblies 60, and the other arm 54 maycomprise a number of broadband sensors 62 for detecting light energyfrom the emitter assemblies 60 that passes through a plastic container66 passing between the arms 52, 54. Thus, light energy from the emitterassembly 60 that is not absorbed by the container 66 may pass throughthe two opposite sidewalls of the container 66 and be sensed by thesensors 62. The container 66 may be rotated through the inspection area20 between the arms 52, 54 by the transfer assembly 12 (see FIG. 1 ). Inother embodiments, a conveyor may be used to transport the containersthrough the inspection area 20.

According to various embodiments, the emitter assemblies 60 comprises apair of light emitting diodes (LED's), laser diodes, etc., that emitlight energy at different, discrete narrow wavelengths bands. Forexample, one LED in each emitter assembly 60 may emit light energy in anarrow band wavelength range where the absorption characteristics of thematerial of the container are highly dependent on the thickness of thematerial of the plastic container 66 (“the absorption wavelength”). Theother LED may emit light energy in a narrow band wavelength that issubstantially transmissive (“the reference wavelength”) by the materialof the plastic container 66. According to various embodiments, there maybe one broadband sensor 62 in the arm 54 for each emitter 60 in the arm52. Based on the sensed energy at both the absorption and referencewavelengths, the thickness through two walls of the container 66 can bedetermined at the height level of the emitter-sensor pair. Thisinformation can be used in determining whether to reject a containerbecause its walls do not meet specification (e.g., the walls are eithertoo thin or too thick). This information can also be used as feedbackfor adjusting parameters of the preform oven 2 and/or the blow molder 6(FIG. 1 ) according to various embodiments, as described further below.

The more closely the emitter-sensor pairs are spaced vertically, themore detailed thickness information can be obtained regarding thecontainer 66. According to various embodiments, there may be betweenthree (3) and fifty (50) such emitter-sensor pairs spanning the heightof the container 66 from top to bottom. For example, there may be thirtytwo emitter-sensor pairs spaced every 0.5 inches or less, althoughadditional emitter-sensor pairs may be used, depending on thecircumstances. Such closely spaced emitter-sensor pairs can effectivelyprovide a rather complete vertical wall thickness profile for thecontainer 66. In some embodiments with closely spaced emitter-sensorpairs, adjacent emitter-sensor pairs may be configured to operate at asmall time offset relative to one another so as to minimize cross-talk.

According to various embodiments, when the inspection system 50 is usedto inspect plastic or PET containers 66, the absorption wavelengthnarrow band may be around 2350 nm, and the reference wavelength band maybe around 1835 nm. Of course, in other embodiments, different wavelengthbands may be used. As used herein, the terms “narrow band” or “narrowwavelength band” means a wavelength band that is less than or equal to200 nm full width at half maximum (FWHM). That is, the differencebetween the wavelengths at which the emission intensity of one of thelight sources is half its maximum intensity is less than or equal to 200nm. Preferably, the light sources have narrow bands that are 100 nm orless FWHM, and preferably are 50 nm or less FWHM.

The arms 52, 54 may comprise a frame 68 to which the emitter assemblies60 and sensors 62 are mounted. The frame 68 may be made of any suitablematerial such as, for example, aluminum. Controllers on circuit boards(not shown) for controlling/powering the emitter 60 and sensors 62 mayalso be disposed in the open spaces defined by the frame 68. Thecrossbar section 56 may be made out of the same material as the frame 68for the arms 52, 54.

The frame 68 may define a number of openings 69 aimed at the inspectionarea 20. As shown in FIG. 2 , there may be an opening for each sensor62. There may also be a corresponding opening for each emitter assembly60. Light energy from the emitter assemblies may be directed throughtheir corresponding opening into the inspection area 20 and toward thesensors 62 behind each opening 69. One example of a system such as thatdescribed above is set forth in U.S. Pat. No. 7,924,421 filed on Aug.31, 2007 and incorporated herein by reference in its entirety.

Another type of measuring device that may be used utilizes a broadbandlight source, a chopper wheel, and a spectrometer to measure the wallthickness of the container as it passes through an inspection areabetween the light source and the spectrometer after being formed by ablow molder. The broadband light source in such a system may providechopped IR light energy that impinges the surface of the plasticcontainer, travels through both walls of the container, and is sensed bythe spectrometer to determine absorption levels in the plastic atdiscrete wavelengths. This information may be used, for example, by aprocessor, to determine characteristics of the plastic bottle, such aswall thickness, material distribution, etc. In practice, such systemsmay use a thermal source to generate broadband light within the visibleand infrared spectrums of interest. The broadband light is chopped,collimated, transmitted through two walls of the plastic container, andfinally divided into wavelengths of interest by the spectroscope.Examples of similar systems are provided in U.S. Pat. No. 6,863,860,filed on Mar. 26, 2002, U.S. Pat. No. 7,378,047, filed on Jan. 24, 2005,U.S. Pat. No. 7,374,713, filed on Oct. 5, 2006, and U.S. Pat. No.7,780,898, filed Apr. 21, 2008, all of which are incorporated herein byreference in their entireties.

In various embodiments, the inspection systems 103 may also includevarious vision and other systems including, for example, a base visionsystem 108, a sidewall vision system 110 a finish vision system 112, abase temperature sensor 114. Optionally, the inspection systems 103 mayalso include sensor systems for directly measuring crystallinity. Forexample, a birefringence sensor 115 may measure crystallinity in coldmold-generated containers. In A near infrared (NIR) spectroscopy sensormay measure crystallinity in hold mold-generated containers. Any or allof the various inspection systems 103 may be configured to operatein-line and inspect the containers as they are formed, as fast as theyare formed, without having to remove the containers from the processingline for inspection and without having to destroy the container forinspection.

The vision system or systems may be similar to the vision system used inthe infrared absorption measurement devices available from AGRInternational, Inc. of Butler, Pa., or as described in U.S. Pat. No.6,967,716, filed on Apr. 21, 2000 which is incorporated herein byreference. FIG. 4 is a block diagram showing one embodiment of a basevision system 108. The system 108 comprises a camera 202, optics 204, alight source 208 and an optional image processor 210. Images may betaken while the container 66 is in the inspection area 21, which may bepositioned between the light source 208 and the camera 202. Resultingimages may be useful, as described herein below, for determining thepresence of haze or pearlescence. Images from the camera 202 may beprovided to an image processor 210, which may perform variouspre-processing and/or evaluate the images to determine properties of thecontainer 66 such as, clarity status (e.g., haze or pearlescencestatus), (various container dimensions, etc.). In some embodiments, theimage processor 210 is omitted and image processing is performed by thecontrol system 102. In the embodiment shown in FIG. 4 , the camera 202and optics 204 are positioned above the container 66. The optics 204 mayinclude various lenses or other optical components configured to givethe camera 202 an appropriate field of view 206 to sense the base area66 a of the container 66 through the finish 66 b. It will be appreciatedthat other configurations of the base vision system 108 are alsopossible. In some embodiments, the positions of the camera/optics202/204 and light source 208 may be reversed. Also, in some embodiments,additional cameras (not shown) having additional fields of view may beutilized.

FIG. 5 is a block diagram showing one embodiment of a sidewall visionsystem 110. The illustrated example sidewall vision system 110 comprisescameras 214, 214′, optics 216, 216′ a light source 212 and the optionalimage processor 210′. Images may be taken while the container 66 is inthe inspection area 21, which may be positioned between the light source212 and the cameras 214, 214′. As illustrated, the cameras 214, 214′ andoptics 216, 216′ are configured to generate fields of view 218, 218′that show sidewall regions 66 c of the container 66. The image processor210′ may perform various processing on images generated by the camera214 including, for example, detecting container defects, detecting theclarity status (e.g., haze or pearlescence status) of the container,etc. In some embodiments, the image processor 210′ performspre-processing on images generated by the camera 214, with furtherprocessing performed directly by the control system 102. Also, in someembodiments, the image processor 210′ may be omitted altogether. Also,in some embodiments, one or more of the cameras 214, 214′ may be omittedand/or additional cameras with additional fields of view (not shown)added.

FIG. 6 is a block diagram showing one embodiment of a finish visionsystem 112. The illustrated example finish vision system 112 comprisescamera 220, optics 222, light sources 224, 226, and the optional imageprocessor 210″. Images may be taken while the container 66 is in theinspection area 21, which may be positioned between the light sources224, 226 and the camera 220. As illustrated, the camera 220 and optics222 are configured to generate a field of view 225 that includes thefinish area 66 b of the container 66. In some configurations, the finishvision system 112 comprises a backlight source 226 positioned in thefield of view 225 to illuminate the finish 66 b. Also, in someembodiments, the finish vision system 112 comprises a round or bowlshaped light source 224 positioned above the vanish 66 b. An imageprocessor 210″ may perform various processing on images including, forexample, deriving from the images various container characteristics(e.g., dimensions, clarity status, etc.). Some or all of the imageprocessing, however, may be performed by the control system 102 and, insome embodiments, the image processor 210″ may be omitted. FIG. 7 is adiagram showing example finish dimensions that may be measured utilizingthe finish vision system 112. For example, the dimension H indicates aheight of the finish. A dimension A indicates a total width of thefinish 66 b. A dimension T indicates a width of the threads 66 e of thecontainer 66. A dimension E indicates a width of the seal 66 f of thefinish.

It will be appreciated that the various vision systems 108, 110, 112 maybe embodied by any suitable type of system capable of generating imagesof the desired portions of the containers 66. For example, the base andsidewall vision systems 108, 110 may be implemented utilizing the PilotVision™ system, available from AGR International, Inc. of Butler, Pa.The finish vision system 112 may be implemented utilizing the Opticheck™system, also available from AGR International, Inc. of Butler, Pa. Itwill further be appreciated that images from additional perspectives maybe obtained by positioning cameras and light sources at differentlocations, for example, within the inspection area 20 or downstream ofthe blow molder system 4.

In some embodiments, outputs of the various vision systems 108, 110, 112are utilized to determine the presence of haze or pearlescence,generally referred to herein as a clarity status. Processing todetermine the clarity status of containers may be performed by thecontrol system 102 and/or by any of the various image processors 210,210′, 210″ described herein. Any suitable image processing algorithm maybe utilized to determine haze or pearlescence status (e.g., the claritystatus) of a container. For example, FIG. 8 is diagram showing an image240 of the container 66 illustrating various methods for determiningclarity status. The image 240 is comprised of a plurality of pixels,where each pixel has a value. For example, when the image 240 is agray-scale image, each pixel may have a value indicating the brightnessof the image at the location of the pixel. When the image is a colorimage, the value of each pixel may indicate color as well as brightness.In FIG. 8 , the emphasis area 242 is reproduced in larger form toillustrate image pixels 243. Gray-scale values for various pixels areindicated by shading. In practice, the control system 102, or othersuitable processor, may identify instances of haze or pearlescence byexamining the images for anomalous pixels. Anomalous pixels may bepixels having a gray-scale or other value that is different from theexpected value, for example, indicating that the container 66 is darkerthan expected. Anomalous pixels may be identified in any suitablemanner. For example, anomalous pixels may be darker than a thresholdvalue and/or greater than a threshold amount darker than the average ofall pixels making up the bottle. Pearlescence or haze may be detected,for example, by identifying a total number of anomalous pixels in thearea representing the container 66 and/or a portion thereof (e.g., abase portion). Also, in some embodiments, a size and or number ofcontiguous groupings of anomalous pixels, such as grouping 244, may beutilized. Results of the algorithm may be expressed in a binary manner(e.g., pearlescence or haze is present; pearlescence or haze is notpresent) or in a quantitative manner, for example, based on the numberof anomalous pixels or pixel groupings.

FIG. 9 is a diagram showing one embodiment of a base temperature sensorsystem 114. The system 114 may comprise a temperature sensor 230positioned with a field of view 232 that includes the base 66 a of thecontainer 66. The temperature of the base 66 a of the container 66 maybe taken while the container 66 is in the inspection area 21, which maybe positioned in the field of view of the temperature sensor 230. Thetemperature sensor 230 may comprise any suitable non-contact or infraredsensor including, for example, any suitable pyrometer, an infraredcamera, etc. Signals from the sensor 230 may be provided to the controlsystem 102 and/or another suitable processor for deriving a basetemperature from the signals. It will be appreciated that various othertemperature sensors may be utilized including, for example, a sidewalltemperature sensor (not shown) with a field of view directed at thesidewall area 66 c of the container 66.

FIG. 10 is a diagram showing one embodiment of a birefringence sensorsystem 115 for measuring crystallinity and/or orientation. Birefringenceis an effect found in many materials, including PET. In someembodiments, a birefringence sensor system 115 may be utilized inconjunction with cold mold-generated containers to measure crystallinity(or orientation) expressed as bi-axial lattice structure. Birefringenceoccurs when linearly polarized light with two orthogonal componentstravel at different rates through a material. Because the orthogonalcomponents travel at different rates through the container 66, there isa resulting phase difference between the two light components. Thedifference in the rates of travel of the light components, and thereforethe observed phase difference, depends on the level of crystallinityexhibited by the container. For example, one component may be consideredthe fast beam and the other a slow beam. The difference in rate, andtherefore phase, is measured as retardance, which is the integratedeffect of birefringence acting along an optical path in a material.Retardance is often measured according to a unit of (nm/cm thickness).Retardance can also be expressed as a phase angle when considering thewavelength of light used.

To measure retardance, a birefringence sensor system 115 may transmitlinearly polarized light through the container 66. The system 115 maycomprise a illumination source 250, a polarizer 252, and a sensor 254.Measurements of crystallinity may be taken while the container 66 is inthe inspection area 21, which may be positioned between the illuminationsource 250 and the sensor 254. For example, the illumination source 250and polarizer 252 may be positioned on one side of the container 66 andconfigured to illuminate the container 66. The sensor 254 may bepositioned on a side of the container 66 opposite the illuminationsource 250 and polarizer 252 and may be configured to receive theillumination provided by the illumination source 250. The polarizer 252may be oriented to cause illumination directed towards the container 66to be linearly polarized with two orthogonal components. For example,the polarizer 252 may comprise two polarizer elements, 252 a, 252 boriented orthogonal to one another about an optical axis 251. In someembodiments, the orientation of the linear polarizer 252 may be rotatedabout 45° relative to the axis of crystallization of the container 66. Asensor 254 opposite the source may receive the light, including the twoorthogonal components. In some embodiments, an optional electricallycontrolled liquid crystal variable polarization device 256 or equivalentthat filters the light is placed between the container 66 and the sensor254. The variable polarization device 256 may be modified to allow thesensor 254 to alternately sense the two formerly orthogonal componentsof the incident beam and thereby measure the phase difference and/ordifference in rate. For example, the angle difference between thepositions of the variable polarization device 256 when measuring the twoformerly orthogonal components may be proportional to the phasedifference. The amount of phase difference per unit thickness of thecontainer walls is the retardance. Accordingly, the end result may be afunction of crystallinity and the thickness of the material. Forexample, the control system 102 may utilize container thickness (e.g.,as measured by the material distribution system 106) to back-out aquantitative measurement of container crystallinity. Although the system115 is illustrated in a configuration that directs the illuminationthrough the sidewall regions 66 c of the container 66, the system 115may be configured to measure birefringence through any suitable portionof the container 66. In some embodiments, the sensor system 115 alsocomprises a processor 258. The processor 258 may, for example, processthe output of the sensor 254 to generate a crystallinity reading for thecontainer 66. In embodiments including the variable polarization device256, the processor 258 may also be in communication with the variablepolarization device 256 to control its polarization value. In variousembodiments, some or all of these functionalities may be executed by thecontrol system 102. For example, the processor 258 may be omitted. Also,any suitable method or apparatus may be used for measuring birefringenceor retardance. Examples of suitable methods and apparatuses formeasuring birefringence or retardance may be found in the followingsources, which are incorporated herein by reference in their entireties:Hagen, et al., “Compact Methods for Measuring Stress Birefringence;” Aiet al., “Testing stress birefringence of an optical window,” SPIE Vol.1531 Advanced Optical Manufacturing and Testing II (1991); Dupaix etal., “Finite strain behavior of poly(ethylene terephthalate) (PET) andpoly(ethylene terephthalaate)-glycol (PETG), Polymer,” Vol. 46, Iss. 13,pgs. 4827-4838 (17 Jun. 2005); and U.S. Pat. No. 5,864,403, filed onFeb. 23, 1998.

FIG. 11 is a diagram showing one embodiment of a near infrared (NIR)spectroscopy sensor system 117 for measuring crystallinity. The system117 may be positioned in the inspection area 20 of the blow moldersystem 4 and/or downstream of the blow molder system 4. In someembodiments, a NIR spectroscopy sensor system 117 may be used inconjunction with hot mold-generated containers to measure crystallinityexpressed as spherulitic structure. The system 117 comprises anillumination source 260 positioned on one side of the container 66 and aspectrometer 262 positioned on another side of the container 66 oppositethe container 66. The illumination source 260 and spectrometer 262 maybe configured to measure absorption through the container 66 over all ora portion of the near infrared spectrum. For example, the illuminationsource 260 and spectrometer 262 may measure absorption across awavelength range of 800 nm to 3000 nm. In some embodiments, theillumination source 260 and spectrometer 262 may measure absorptionacross a wavelength range of 2000 nm to 2400 nm.

The illumination source 260 and spectrometer 262 may be tuned to aparticular wavelength or wavelength range in any suitable manner. Forexample, the illumination source 260 may be a broadband sourcegenerating illumination across the desired wavelength range. Thespectrometer 262 may be configured to measure the intensity of theillumination (e.g., after transmission through the container 66) atdifferent wavelengths. For example, the spectrometer 262 may comprise adiffraction grating 266 or other suitable optical device for separatingreceived illumination by wavelength across the desired range (e.g.,spatially separating the received illumination by wavelength). Acontrollable micromirror 268 or other similar device may direct aportion of the spatially separated illumination corresponding to awavelength or wavelength range to a sensor 269, such as an InGaAsdetector. The sensor 269 may provide an output signal proportional tothe intensity of the received illumination at the wavelength orwavelength range directed to the sensor 269 by the micromirror 268. Themicromirror 268 may be progressively tuned to direct differentwavelengths or wavelength ranges to the sensor 269, providing a set ofsignals from the sensor 269 that indicate absorption of the illuminationby the container 66 over the desired wavelength range. This may bereferred to as an absorption spectrum or spectrum for the container 66.For example, the amount of illumination that is transmitted by thecontainer 66 at any given wavelength may be the inverse of theabsorption of the container 66 at that wavelength.

A processor 264 may be configured to control the micromirror 268 and/orreceive and store signals from the sensor 269 to determine theabsorption spectrum for the container 66. In some embodiments, some orall of the functionality of the processor 264 may be performed by thecontrol system 102. For example, the processor 264 may be omitted. Also,although the illumination is shown to intersect the container 66 at thesidewall region 66 c, the absorption spectrum may be taken at anysuitable portion of the container 66. Also, FIG. 11 shows just oneexample spectrometer 262. Any suitable type of spectrometer may be used.

Captured absorption spectra may correlate to the level of crystallinityin the molecules of the container 66. The processor 264 and/or controlsystem 102 may utilize absorption spectra, as measured by the (NIR)spectroscopy sensor system 117, to measure the crystallinity ofcontainers 66 in any suitable manner. FIG. 12 is a flow chart showingone embodiment of a process flow 270 that may be executed by the system100 to generate a crystallinity model that relates NIR absorptionspectra to crystallinity. For example, the process flow 270 may beexecuted by the processor 264 and/or the control system 102. At 272, thesystem 100 may generate a set of calibration spectra. For example, thesystem 100 may receive a set of calibration containers 66 having knowncrystallinity. For example, the calibration containers 66 may have hadtheir crystallinity measured ahead of time using a off-line or lab basedmethod such as, for example, a birefringence method, X-ray diffraction,Raman spectroscopy, near infrared (NIR) absorption spectroscopy, etc.

The calibration containers 66 may be processed by the system 102 usingthe NIR spectroscopy sensor system 117 as described herein above togenerate an absorption spectrum for each of the calibration containers66. For example, FIG. 13 is a chart 290 showing one embodiment of a setof calibration spectra 292. The chart 290 comprises a horizontal axis294 indicating wavelength and a vertical axis 296 indicating theamplitude of the spectra 292. The indicated wavelength range is between2000 nm and 2455 nm, although in some embodiments the spectra may betaken between 2000 nm and 2400 nm.

As described herein, the calibration spectra 292 may indicatecrystallinity. For example, crystallinity may be indicated by theamplitude of the spectra at different wavelengths, the wavelengthposition of amplitude peaks and troughs, etc. In addition tocrystallinity information, however, the absorption spectra 292 may alsoinclude artifacts indicative of other factors including process factorssuch as direct current (DC) bias, tilt of the various components of thesystem 117, scattering effects, illumination path length, etc. as wellas other properties of the container 66 such as, for example, thematerial make-up of the container 66, the material thickness of thecontainer, etc. Referring again to FIG. 12 , at 274, the system 100 maycondition the calibration spectra 292 to remove contributions fromprocess factors. The conditioning may take any suitable form. Forexample, the system 100 may apply centering, scaling, smoothing,derivatizing (first and/or second derivatizing), varioustransformations, baselining, etc. FIG. 14 is a chart 291 showing oneembodiment of the calibration spectra 292 of FIG. 13 after conditioning.

Referring again to FIG. 12 , at 276, the system 100 may utilize theconditioned calibration spectra 293 to generate a model relating theconditioned calibration spectra 293 to container crystallinity. Themodel may be generated according in any suitable manner according to anysuitable method. In some embodiments, the model may be generatedaccording to a partial least squares or PLS method. For example,performance data indicating the measured crystallinity of each of thecalibration containers may make up an independent variable or x-block ofdata. The conditioned calibration spectra 293 may represent a dependentvariable or y-block. A PLS model may be generated relating the x-blockto the y-block. The model may include any suitable number of terms. Forexample, in some embodiments, the number of terms in the model may beselected to provide a desired degree of precision.

The model generated by the process flow 270 may be configured to receivea dependent variable (e.g., a conditioned spectrum from a container 66)and output a corresponding crystallinity for the container 66. In thisway, the model may facilitate direct determinations of crystallinity forcontainers 66 in-line. FIG. 15 is a flow chart showing one embodiment ofa process flow 280 for determining the crystallinity of a containerin-line utilizing the model determined by the process flow 270. Theprocess flow 280 may be executed by the system 100 such as, for example,by the control system 102 and/or the processor 264. At 282, the system100 may receive an absorption spectrum for a container 66. Theabsorption spectrum may be captured by the NIR spectroscopy sensorsystem 117 described herein, for example, as the container is in-line.At 284, the system may condition the received spectrum. For example, thesystem 100 may apply the same conditioning, described at 274 above, thatwas applied to the calibration spectra 292 to generate the model. At286, the system 100 may apply the calibration model to determine acrystallinity of the container 66.

In various embodiments, the control system 102 may be programmed tointegrate results from the various inspection systems 103 to theparticular molds and spindles within the blow molder 6, for example, asdescribed in U.S. Pat. No. 7,374,713, which is incorporated herein byreference in its entirety.

As described herein, the control system 102 may implement a singlecontrol loop or multiple control loops. FIG. 16 is a block diagramshowing one embodiment of a work flow 300 that may be executed by thecontrol system 102 to control the blow molder system 4 with a singlecontrol loop. A model 304 receives as inputs various containercharacteristics 302 describing containers generated by the blow molder.In the embodiment shown in FIG. 16 , the input container characteristicsare a material distribution 308, a clarity status 310. The claritystatus may indicate a haze status if the model 304 is used in a hot moldprocess or a pearlescence status if the model 304 is used in a cold moldprocess. In addition to or instead of the clarity status, the model 304may, in some embodiments, receive a direct measurement of containercrystallinity 311. Based on the inputs 302, the model 304 producesvalues for blow molder input parameters 306 that are provided to theblow molder system 4 by the control system 102 (FIG. 2 ). FIG. 16 listsexample blow molder input parameters. It will be appreciated, however,that the set of blow molder input parameters 306 predicted by the model304 need not be identical to that shown and may include additionalparameters or omit some of the parameters shown in FIG. 16 .

The model 304 may be any suitable type of model and may be generated inany suitable manner. For example, the model 304 may be possible becauseof a strong R² correlation between the inputs 302 and the blow molderinput parameters 306. The model 304 may be implemented and trained inany suitable manner. For example, FIG. 17 is flow chart showing oneembodiment of a process flow 1100 for training the model 304. Theprocess flow 1100 may be executed, for example, by the control system102. At 1102, the system 102 may measure characteristics of containersgenerated by the blow molder system 4. For example, the materialdistribution 308 may be measured in conjunction with the materialdistribution system 106. The clarity status 310 may be measured inconjunction with one or more of the vision systems 108, 110, 112. Thecrystallinity may be measured by the birefringence sensor system 115and/or the NIR spectroscopy system 117. In some embodiments, theoperation of the blow molder system 4 is tuned (e.g., manually) prior tomeasuring the one or more containers such that the material distributionand clarity status of the measured containers is correct. Accordingly,the measured containers may establish a baseline material distributionand clarity status for the model.

In some embodiments, additional tuning may be performed relative to thecrystallinity and clarity status before taking the baselinecharacteristic measurements at 1102. In a cold mold process, forexample, the control system 102 may decrease the preform temperature setpoint until pearlescence appears (e.g., until the clarity statusindicates that pearlescence is present).

Then the control system 102 may increase the preform temperature setpoint until pearlescence is no longer present. Subsequently, the controlsystem may take the baseline measurements at 1102. Similarly, for a hotmold process, the control system 102 may increase the preformtemperature set point until haze appears (e.g., until the clarity statusindicates that haze is present). Then the control system 102 maydecrease the preform temperature set point until pearlescence is nolonger present before taking the baseline measurements at 1102. This mayensure that the baseline measurements for the model 304 are taken withcrystallinity at or near its optimal value. Also, in variousembodiments, the control system 102 may be programmed to periodicallyperform the described clarity tuning during operation of the blow moldersystem 4. This may correct for process drift, which may tend to push theblow molder system 4 away from generating containers at optimalcrystallinity. In some embodiments, the baseline measurements at 1102may be taken with the blow molder system 4 tuned to generate containerswith small, but acceptable, levels of haze or pearlescence. This maydrive the model 304 to generate containers with optimal crystallinity,as described herein.

At 1104, the control system 102 may record (e.g., store in memory) thecontainer characteristics 302 of each generated container along withvalues of the blow molder input parameters 306 for the blow moldersystem 4 at the time that each container was produced. These values maybe entered into a multi-dimensional matrix that may be used, forexample, as described herein below. At 1106, the control system 102 maygenerate the model 304 relating blow molder input parameters andcontainer characteristics. For example, the control system 102 mayutilize the matrix to derive the model of blow molder system 4parameters versus resulting container characteristics. The model 304 maybe generated using any suitable technique or techniques. Examplemodeling techniques that may be used include, for example, linearregression methods, stepwise regression, principle componentsregression, etc. In some embodiments, the relationship between blowmolder input parameters and material distribution indicated by the modelis a relationship between desired changes in material distribution andcorresponding changes in blow molder input parameters.

Optionally, the model may be tested or validated upon generation at1106. For example, if the model generates blow molder input parametersthat are out of an acceptable range, or the characteristics of thecontainers generated during the actions 1102, 1104 do not represent anacceptable baseline, the control system 102 may modify the blow molderinput parameters at 1108 and generate new containers at 1110. Thecontrol system 102 may measure and/or derive the containercharacteristics at 1102, record (e.g., store in memory) the containercharacteristics 302 and new blow molder input parameters 306 at 1604(e.g., to the multi-dimensional matrix) and determine, again, if themodel 304 validates at 1106. In some embodiments, this process isrepeated until the model 304 validates.

Once the model 304 is generated, it may be used to modify blow moldersystem 4 parameters based on the characteristics of completed containers302. For example, the control system 102 may be programmed to drive thecharacteristics 302 of produced containers to a baseline materialdistribution. If the container characteristics of the producedcontainers deviate from the baseline (e.g., by more than a thresholdamount), the control system 102 may utilize the model to determine ablow molder system 4 control parameter or parameters that may bemodified to move the material distribution of subsequently producedcontainers back towards the baseline material distribution. For example,the material distribution of containers generated by the blow moldersystem 4 may drift due to changes in the conditions of or at the blowmolder system 4.

FIG. 18 is a flow chart showing one embodiment of a process flow 350that may be executed by the control system 102 to apply the model 304 tomodify the blow molder input parameters 306 based on the containercharacteristics 302 of containers produced by the blow molder system 4.At 352, the control system 102 may receive values for the containercharacteristics 302. In some embodiments, as described herein, thecontrol system 102 may perform processing on input signals from theinspection systems 103 to derive container characteristics, such asmaterial distribution, clarity status, crystallinity, etc. At 354, thecontrol system 102 may utilize the model 304 to derive blow molder inputparameters. For example, the control system 102 may calculate an errorsignal representing a difference between the container characteristicsof generated containers received and/or derived at 352 and the baselinecharacteristics measured during model training, as described withrespect to FIG. 11 . The error signal may represent a desired change inthe container characteristics generated by the blow molder system 4. Theerror signal may be applied to the model 304, which may return changesthat can be made to the blow molder system 4 input parameters to bringabout the desired changes and drive the container characteristics backto the baseline. For example, utilizing the relationship betweencontainer characteristics 302 and blow molder input parameters 306, thecontrol system 102 may derive blow molder input parameters 306 thatminimize the difference between the container parameters and thebaseline container parameters (e.g., the error signal) while alsominimizing the differences between the derived blow molder inputparameters 306 and the current parameters applied at the blow moldersystem 4. At 356, the control system 102 may apply the derived blowmolder input parameters 306 to the blow molder system 4.

As described above, the initial baseline material distribution may bebased on the containers measured to generate the model. In someembodiments, the model and/or an additionally generated model, may beused to correlate material distribution values to section weights, forexample, as described in co-pending U.S. Patent Application PublicationNo. 2012-0130677, filed on Nov. 18, 2011 and incorporated herein byreference in its entirety.

A model generated in the manner described with respect to FIGS. 16-17and applied as described with respect to FIG. 18 may be configured tomake changes to the preform temperature set point so as to optimizecrystallinity according to the determined baseline. For example, whenused for hot mold processes, the model 304 may tend to decrease thepreform temperature set point, or equivalent blow molder inputparameter, when the clarity status indicates the presence of haze. Also,when used for cold mold processes, the model 304 may tend to increasethe preform temperature set point, or equivalent blow molder inputparameter, when the clarity status indicates the presences ofpearlescence. When a direct crystallinity measurement is used inaddition to or instead of a haze or pearlescence measurement, the model304 may tend to drive the container crystallinity to a desired value,for example, as set by the baseline.

It will be appreciated that the model 304 may be further expanded toconsider additional input container characteristics 302. For example,FIG. 19 is a flow chart showing one embodiment of the work flow 300 ofFIG. 16 including additional input container characteristics. FIG. 19shows a base temperature characteristic 312 and a container dimensioncharacteristic 314. The base temperature characteristic 312 may beutilized, for example, in cold mold processes to manage base sag. Forexample, the base temperature 312 may be indicative of either an energyof the container base and/or an amount of material in the containerbase. As described herein, high values for either are consistent with ahigh likelihood that the containers will exhibit base sag. The containerdimension characteristics 314 may be indicative of any dimension on anyportion of the container. For example, the container dimensioncharacteristics 314 may include one or more finish dimensions, forexample, as described herein with respect to FIG. 7 . To incorporateadditional inputs 314, 316, the blow molder system 4 may be tuned asdescribed above (e.g., manually) to achieve baseline values for theinputs 314. Then the model may be developed and trained, for example, asdescribed herein with respect to FIGS. 16 and 17 and applied, asdescribed herein with respect to FIG. 18 .

In some embodiments, the model 304 may take additional inputs such as amold temperature input 316. The mold temperature 316 may be measured bya separate sensor, such as a pyrometer or infrared camera, directed atone or more of the molds of the blow molder 6. In some embodiments, theblow molder 6 may be manufactured with a built-in sensor or sensors formeasuring mold temperature. The control system 102 may, then, receivethe mold temperature directly from the blow molder. A baseline value forthe mold temperature may also be set prior to the model generation, asillustrated in FIGS. 16 and 17 . For example, the baseline moldtemperature may be the mold temperature when the containercharacteristics are otherwise at desirable values.

FIG. 20 is a block diagram showing one embodiment of a work flow 400that may be executed by the control system 102 to control the blowmolder system 4 utilizing two control loops 401, 403. A materialdistribution control loop 403 may implement the model 304 as describedabove to generate blow molder parameters 306′ that may be provided tothe blow molder system 4 to drive the material distribution of generatedcontainers towards a desired baseline distribution. In the materialdistribution control loop 403, however, the model 304 may be generatedand tuned to drive a set of blow molder parameters 306′ that excludesthe preform temperature set point 404 and/or equivalent blow molderparameters, which may be driven by the preform temperature control loop401. The preform temperature control loop 401 comprises a preformtemperature algorithm 402, executed by the control system 102, whichreceives as input one or more container characteristics, such as thecrystallinity 311, the clarity status 310, the base temperature 312 andone or more container dimensions 314, and generates as output thepreform temperature set point 404 or equivalent parameter. In this way,the material distribution control loop 403 may drive blow molder inputparameters 306′ that affect material distribution, while the preformtemperature control loop 401 may drive blow molder input parameters(e.g., the preform temperature set point or equivalent 404) that affectclarity, base sag, and certain container dimensions. Additional inputcontainer characteristics, for example, as described herein above, maybe incorporated into either the material distribution control loop 403and/or the preform temperature control loop.

FIG. 21 is a flow chart showing one embodiment of a set of process flows420, 350 for executing the material distribution control loop 403 andpreform temperature control loop 401 shown in FIG. 20 in a cold moldprocess. Process flow 420 is an example implementation of the preformtemperature algorithm 402 of the preform temperature control loop 401.Process flow 350 is an example implementation of the materialdistribution control loop 403. The process flows 420, 350 may beexecuted by the control system 102, for example, simultaneously tocontrol the relevant blow molder input parameters. In this way, anyunintended changes to material distribution that result from applicationof the process flow 420 as part of the preform temperature control loop401 may be corrected by the algorithm 350 as part of the materialdistribution control loop 403. The process flow 350 may be similar tothe process flow 350 described above with respect to FIG. 18 . Forexample, the material distribution control loop 403 may execute themodel 304 in a manner similar to that described above with respect toFIGS. 16, 17 and 18 to drive the material distribution of containersproduced by the blow molder towards a baseline material distribution.

Referring to the process flow 420, at 421, the control system 102 mayreceive an indication of crystallinity. The indication of crystallinitymay be a direct indication of crystallinity, for example, as measured bya birefringence sensor system such as 155, or may be an indirect measureof crystallinity such as a pearlescence or clarity status. Ifcrystallinity is above a threshold level, then the control system mayincrease the preform temperature set point at 424. When the indicationof crystallinity is a direct measure of crystallinity, the thresholdlevel may be a particular value or range of values for crystallinity.When the indication of crystallinity is a pearlescence or claritystatus, the threshold level may be when the pearlescence status ispresent (e.g., there is pearlescence detected in the output containers)and/or above a pearlescence threshold level. When the indication ofcrystallinity is above the threshold, the control system 102 mayincrease the preform temperature set point at 424. For example, thepreform temperature set point may be increased by a predeterminedincrement. If the indication of crystallinity is not above thethreshold, then the control system 102 may return to action 421 andcontinue to execute the process flow 420. In this way, the preformtemperature control loop 401 may control the preform temperature setpoint to drive the blow molder system 4 away from generating containersthat exhibit pearlescence. In some embodiments, when the indication ofcrystallinity is not above the threshold, the control system 102 mayreduce the preform temperature set point before returning to action 421.In embodiments where a direct crystallinity measurement is taken, theprocess flow 401 may comprise a decision step where the control system102 compares the container crystallinity to a desired threshold orrange. If the crystallinity is above the desired threshold or range,then the preform temperature set point may be increased. If thecrystallinity is below the desired threshold or range, then the preformtemperature set point may be maintained or decreased. This may occur inaddition to or instead of the comparison of pearlescence to thethreshold at 422.

Optionally, the process flow 420 may comprise additional steps forreorienting the process towards optimal crystallinity (e.g., the lowestpreform/mold temperature that avoids pearlescence). For example, insteadof proceeding back to 421 upon determining at 422 that the crystallinityis not above the threshold, the control system 102 may determine at 426whether there has been a system change since the last time (if any) thatthe crystallinity was above the threshold. If at 426, the control system102 determines that the crystallinity has been above the threshold sincethe last system change, then the control system 102 may proceed to 421,as descried above. A system change may indicate that the systemconditions have changed in a way that may have moved containercrystallinity, but in a direction that does not cause pearlescence orhaze. For example, a system change may be indicated the first time thatthe process flow 420 is executed. Also, for example, a system change maybe indicated when the control system 102 receives an indication that thetemperature of generated containers or the temperature of the mold haschanged. Such a change in container temperature may be indicated by adirect measurement of container temperature (e.g., a base temperature)and/or from a measurement of the mold temperature. In some embodiments,a system change may be indicated when the crystallinity of the producedcontainers drops below a threshold value.

If at 426, the control system 102 determines that crystallinity has notbeen above the threshold since the last system change, the controlsystem 102 may decrease the preform set temperature at 428 and thenproceed back to 421. This may tend to drive the blow molder system 4closer to the crystallinity threshold, but also towards the optimalcrystallinity. If a decrease in preform temperature set point at 428pushes the blow molder system 4 past the crystallinity threshold, thenthe resulting pearlescence will be detected at 422, resulting in acorrective increase in preform temperature set point at 424. In someembodiments, described herein, a clarity status may be utilized in lieuof actively finding the crystallinity of the containers. Also, in someembodiments, decision action 426 may be omitted, and the control system102 may decrease the preform set point at 428 any time that thecrystallinity is above the threshold.

FIG. 22 is a flow chart showing one embodiment of a process flow 430representing an alternate implementation of the preform temperaturealgorithm 420 of the preform temperature control loop 401 that alsoconsiders a container base temperature in a cold mold process. At 431,the control system 102 may receive a pearlescence status of containersgenerated by the blow molder system 4. At 432, the control system 102may receive a base temperature reading of containers generated by theblow molder system 4. In some embodiments, the base temperature readingand crystallinity may be received in any order, or simultaneously. Ifthe crystallinity indicates is above the threshold at 434, then thecontrol system 102 may increase the preform temperature set point at 436and return to action 431. If the crystallinity is not above thethreshold, the control system 102 may determine whether the basetemperature is greater than a threshold base temperature at 438. If thebase temperature is greater than the threshold base temperature, thenthe control system 102 may decrease the preform temperature set point at440 and return to action 431. If the base temperature is not greaterthan the threshold base temperature, then the control system 102 mayreturn to action 431 without decreasing the preform temperature setpoint.

The algorithm 430 also comprises optional steps for reorienting theprocess towards optimum crystallinity upon detection of a system change.For example, instead of proceeding to 431 after determining that thebase temperature is not greater than the threshold, the control system102 may determine, at 442, whether there has been a system change sincethe last time that the crystallinity met the threshold. If not, then thecontrol system 102 may proceed to 431. If so, then the control system102 may decrease the preform temperature set point at 440 beforereturning to 431. It will be appreciated that the order of the actions434, 438 may be reversed so as to reverse the relative importance ofcrystallinity versus base temperature. For example, in an applicationwhere it is relatively more important to avoid base sag than it is toavoid pearlescence, then action 438 may be executed prior to action 434.Also, it will be appreciated that additional input values may beaccommodated by utilizing additional decision steps similar to 434, 438.

FIG. 23 is a flow chart showing one embodiment of a process flow 450representing an alternate implementation of the preform temperaturealgorithm 402 of the preform temperature control loop 401 in a hot moldconfiguration. At 451, the control system 102 may receive an indicationof crystallinity (e.g., a direct crystallinity measurement and/or aclarity status indicating the presence or absence of haze). If theindication of crystallinity is above a threshold at 452, the controlsystem 102 may decrease the preform temperature set point at 454. Whenthe indication of crystallinity is a direct crystallinity measurement,it may be above a threshold when it is above a particular value. Whenthe indication of crystallinity is a clarity or haze status, it may beabove the threshold, for example, if the clarity status indicates thathaze is present and/or that haze is above a threshold level. Ifindication of crystallinity is not above the threshold at 452, thecontrol system 102 may return to action 451.

Optionally, the control system 102 may execute a control sequence forreorienting the process near the optimal crystallinity. For example,when indication of crystallinity is below the threshold at 452, insteadof returning to 451, the control system 102 may determine, at 456,whether there has been a system change since the last time that thecrystallinity was above the threshold. A system change may be indicatedby various different values including, for example, a change in thetemperature of containers generated by the blow molder system 4 (e.g., asidewall temperature), a change in a temperature of the mold or molds,etc. If there has been no system change since the last time that thehaze status was present, then the control system 102 may return toaction 451. If there has been a system change since the last time thatthe haze status was present, then the system 102 may increase thepreform temperature set point at 458 and then proceed to action 451.Optionally, action 456 may be omitted and the preform temperature setpoint may be decreased at 458 when it is determined at 452 that the hazestatus is present and/or greater than the haze threshold.

FIG. 24 is a flow chart showing one embodiment of a process flow 460representing an alternate implementation of the preform temperaturealgorithm 402 of the preform temperature control loop of FIG. 20 in ahot mold configuration and including additional actions that correct forcontainer dimensions. At 461, the control system 102 may receive theindication of crystallinity for containers generated by the blow moldersystem 4. At 462, the control system 102 may receive an indication of acontainer dimension for containers generated by the blow molder system4. It will be appreciated that the control system 102 may receive theindication of crystallinity at 461 and the container dimension at 462 inany order, or simultaneously. If, at 464, the indication ofcrystallinity is greater than the threshold, then the control system 102may decrease the preform temperature set point at 470 and return to 461.If the crystallinity is not greater than the threshold at 464, thecontrol system 102 may determine at 476 whether one or more containerdimensions are less than or equal to a desired dimension threshold. Asdescribed herein above, this may indicate that the preforms are not hotenough to generate an adequate mold fill factor. If the measureddimension or dimensions are less than the threshold, then the controlsystem 102 may increase the preform temperature set point at 474. Ifnot, then the control system 102 may proceed to 461.

In an optional control sequence for reorienting the process near theoptimal crystallinity, instead of returning to 461 when the containerdimensions are not below the threshold, the control system may proceedto 472. If at 472 the control system 102 determines that there have notbeen any system changes the last time (if any) that the indication ofcrystallinity was above the threshold, then the control system 102 mayreturn to 461. If there has been a system change since the last timethat the indication of crystallinity was above the threshold, then thecontrol system may increase the preform temperature set point at 474before returning to 461. It will be appreciated that the relativeimportance of the variables of the process flows 450 and 460 may bechanged by changing the orders in which their respective decision stepsare considered. Also, additional decision steps for additional variablesmay be added and/or some of the decision steps for the describedvariables omitted, depending on the implementation.

The examples presented herein are intended to illustrate potential andspecific implementations of the embodiments. It can be appreciated thatthe exemplary embodiments are intended primarily for purposes ofillustration for those skilled in the art. No particular aspect oraspects of the examples is/are intended to limit the scope of thedescribed embodiments.

As used in the claims, the term “plastic container(s)” means any type ofcontainer made from any type of plastic material including, polyethyleneterephthlat (PET), oriented polypropolyene (OPP), etc.

It is to be understood that the figures and descriptions of theembodiments have been simplified to illustrate elements that arerelevant for a clear understanding of the embodiments, whileeliminating, for purposes of clarity, other elements. For example,certain operating system details and power supply-related components arenot described herein. Those of ordinary skill in the art will recognize,however, that these and other elements may be desirable in inspectionsystems as described hereinabove. However, because such elements arewell known in the art and because they do not facilitate a betterunderstanding of the embodiments, a discussion of such elements is notprovided herein.

In general, it will be apparent to one of ordinary skill in the art thatat least some of the embodiments described herein may be implemented inmany different embodiments of software, firmware and/or hardware. Thesoftware and firmware code may be executed by a processor (such as aprocessor of the control system 102, the various image processors 210,etc.) or any other similar computing device. The software code orspecialized control hardware which may be used to implement embodimentsis not limiting. The processors and other programmable componentsdisclosed herein may include non-transitory memory for storing certainsoftware applications used in obtaining, processing and communicatinginformation. It can be appreciated that such non-transitory memory maybe internal or external with respect to operation of the disclosedembodiments. The memory may also include any means for storing software,including a hard disk, an optical disk, floppy disk, ROM (read onlymemory), RAM (random access memory), PROM (programmable ROM), EEPROM(electrically erasable PROM) and/or other computer-readable media.

In various embodiments disclosed herein, a single component may bereplaced by multiple components and multiple components may be replacedby a single component, to perform a given function or functions. Exceptwhere such substitution would not be operative, such substitution iswithin the intended scope of the embodiments. For example, processor 142may be replaced with multiple processors.

While various embodiments have been described herein, it should beapparent that various modifications, alterations and adaptations tothose embodiments may occur to persons skilled in the art withattainment of at least some of the advantages. The disclosed embodimentsare therefore intended to include all such modifications, alterationsand adaptations without departing from the scope of the embodiments asset forth herein.

We claim:
 1. A blow molder system comprising: a blow molder forperforming a blow molding process, wherein the blow molder comprises:heating elements; a stretching rod; and a plurality of molds; and one ormore blow molder temperature sensors for sensing temperatures from theblow molder; and wherein the blow molding process comprises a pre-blow,stretching by the stretching rod, and a high-pressure blow; aninspection system for sensing one or more characteristics of plasticobjects involved in the blow molding process; and a control system thatis in communication with the one or more blow molder temperature sensorsand in communication with the inspection system, wherein the controlsystem is configured to: determine, using a model, updated operatingparameters for the blow molder based on, at least in part, the one ormore characteristics of the plastic objects sensed by the inspectionsystem; and output the updated operating parameters to the blow molder,wherein: the blow molder is configured to implement the updatedoperating parameters; and the updated operating parameters comprise: anupdated pre-blow pressure level for the pre-blow; an updatedhigh-pressure blow pressure level for the high-pressure blow; and a blowtiming for the blow molding process.
 2. The blow molder system of claim1, wherein: the blow molder produces plastic containers from plasticpreforms; and the one or more characteristics comprise one or morecharacteristics of the containers produced by the blow molder.
 3. Theblow molder system of claim 2, wherein the plastic objects comprise theplastic containers produced by the blow molder.
 4. The blow moldersystem of claim 1, wherein: the inspection system further comprises avision system for capturing images of the plastic objects; and thecontrol system is further configured to determine the updated operatingparameters additionally based on the images captured by the visionsystem.
 5. The blow molder system of claim 1, wherein the control systemis further configured to determine the updated operating parametersadditionally based on temperatures sensed by the one or more blow moldertemperature sensors.
 6. The blow molder system of claim 1, wherein theheating elements of the blow molder comprise oven lamps.
 7. The blowmolder system of claim 6, wherein the one or more blow moldertemperature sensors comprises one or more pyrometers.
 8. The blow moldersystem of claim 1, wherein the control system is further configured todetermine the updated operating parameters additionally based on thetemperatures of the plurality of molds sensed by the one or more blowmolder temperature sensors.
 9. The blow molder system of claim 2,wherein the control system is configured to control the blow molderparameters to achieve a desired quality level for the containersproduced by the blow molder.
 10. The blow molder system of claim 9,wherein the blow molder stretches the preforms by blowing fluid into thepreforms to stretch the preforms to form the containers.
 11. The blowmolder system of claim 10, wherein the fluid comprises air.
 12. The blowmolder system of claim 1, wherein: the blow molder comprises a liquidfor controlling temperatures of the plurality of molds; the one or moreblow molder temperature sensors comprise one or more mold temperaturesensors; and the control system is further configured to determine theupdated operating parameters additionally based on the temperatures ofthe plurality of molds sensed by the one or more mold temperaturesensors.
 13. The blow molder system of claim 1, wherein the updatedoperating parameters determined by the control system comprisetemperature set points for the plurality of molds.
 14. The blow moldersystem of claim 1, wherein the control system comprises a server. 15.The blow molder system of claim 1, wherein the model comprises aregression model.
 16. The blow molder system of claim 1, wherein theplastic objects comprise polyethylene terephthalate.
 17. The blow moldersystem of claim 9, wherein the desired quality level comprises amaterial distribution for the containers.
 18. The blow molder system ofclaim 1, wherein: the inspection system further comprises a visionsystem for capturing images of the plastic objects; and the controlsystem comprises a processor that is configured to detect defects in theplastic objects from the images captured by the vision system.
 19. Theblow molder system of claim 18, wherein the vision system comprises anoverhead vision system that captures images from above and lookingdownwardly on the plastic objects.
 20. The blow molder system of claim18, wherein the vision system comprises a side vision system thatcaptures images of a sidewall of the plastic objects.